In this problem, you will perform K-means clustering (using Euclidean distance) manually, with K The observations are as follows. 2, on a small example with n 6 observations and p 2 features. Set (1,4) as the centroid for cluster 1 and (1,3) as the centroid for cluster 2. Then (a) assign each observation to the nearest cluster. How many points will be assigned to cluster 1? Ans: How many points will be assigned to cluster 2? Ans: (b) update the centroids for the two clsuters. What's the x-coordinate of the new centroid for cluster 1? What's the x-coordinate of the new centroid for cluster 2? Repeat (a) and (b) until convergence. After the algorithm converges, the x-coordinate of the cluster centroid to which (4,0) belongs is equal to the size of the cluster to which (4,0) belongs is , and i.e., number of points in that cluster including (4,0)